library(plotly);
library(dplyr);
library(ggplot2);
data(diamonds, package = "ggplot2");
set.seed(37);
diamonds1 = sample_n(diamonds,size=500);
p <- ggplot(diamonds1, aes(x = log(carat), y = log(price),color=cut))+
  geom_point();
p

ggplotly(p);

p <- ggplot(diamonds1, aes(x = log(price), color = clarity)) + 
    geom_freqpoly(bins=15);
p

ggplotly(p);

p <- ggplot(diamonds1, aes(x = log(price), color = clarity)) + 
    geom_freqpoly(stat = "density") + 
    facet_wrap(~cut);
p

ggplotly(p);

library(GGally);
data(economics, package = "ggplot2");
dim(economics);

str(economics);

p <- ggpairs(economics[,3:6]);
p

ggplotly(p)

library(plotly)

p <- plot_ly(economics,
         type = "scatter",       # all "scatter" attributes: https://plotly.com/r/reference/#scatter
         mode = "markers",       # the drawing mode for this scatter trace
         x = ~date,              # more about scatter's "x": https://plotly.com/r/reference/#scatter-x
         y = ~uempmed,           # more about scatter's "y": https://plotly.com/r/reference/#scatter-y
         name = "unemployment",  # more about scatter's "name": https://plotly.com/r/reference/#scatter-name
         marker = list(          # marker is a named list, valid keys: https://plotly.com/r/reference/#scatter-marker
         color="#264E86"        # more about marker's "color" attribute: https://plotly.com/r/reference/#scatter-marker-color
                                # see https://htmlcolorcodes.com/ for colors
               )) %>%
  add_trace(x = ~date,                                         # scatter's "x": plotly.com/r/reference/#scatter-x
            y = ~fitted((loess(uempmed ~ as.numeric(date)))),  # scatter's "y": plotly.com/r/reference/#scatter-y
            mode = 'lines+markers',                            # scatter's "y": plotly.com/r/reference/#scatter-mode
            line = list(                                       # line is a named list, valid keys: /r/reference/#scatter-line
            color = "#F10D2F",                                 # line's "color": plotly.com/r/reference/#scatter-line-color
            dash = "dashed"                                   # line's "dash" property: plotly.com/r/reference/#scatter-line-dash
                ), 
            marker = list(color = "#F10D2F")
      ) %>%
  layout(                        # all of layout's properties: /r/reference/#layout
         title = "Unemployment", # layout's title: /r/reference/#layout-title
         xaxis = list(           # layout's xaxis is a named list. List of valid keys: plotly.com/r/reference/#layout-xaxis
            title = "Time",      # xaxis's title: plotly.com/r/reference/#layout-xaxis-title
            showgrid = F),       # xaxis's showgrid: plotly.com/r/reference/#layout-xaxis-showgrid
         yaxis = list(           # layout's yaxis is a named list. List of valid keys: plotly.com/r/reference/#layout-yaxis
             title = "uidx")     # yaxis's title: /r/reference/#layout-yaxis-title
      )

p

p=ggplot(data=economics, aes(x=date, y=uempmed) )+
  geom_point()+
  geom_smooth(formula=y~x, method="loess", se=F)+
  ggtitle("Unemployment")+
  theme(plot.title=element_text(hjust = 0.5))+#center the title
  labs(x="Time", y="uidx");
ggplotly(p)

diamonds%>%group_by(clarity)%>%summarize(freq = n())%>% #
  plot_ly(labels = ~clarity, values = ~freq)%>%
  add_pie(hole = 0.5, text=~clarity)%>%
  layout(title = "Clarity of diamonds", showlegend =F);

diamonds%>%group_by(cut)%>%summarize(freq = n())->data1;
diamonds%>%group_by(color)%>%summarize(freq = n())->data2;
diamonds%>%group_by(clarity)%>%summarize(freq = n())->data3;

fig <- plot_ly();
fig<-fig %>%add_pie(data=data1, labels = ~cut, values = ~freq, text=~cut, domain = list(x = c(0, 0.4), y = c(0.4, 1)));

fig<-fig %>%add_pie(data=data2, labels = ~color, values = ~freq,text=~color, domain = list(x = c(0.6, 1), y = c(0.4, 1)));

fig<-fig %>%add_pie(data=data3, labels = ~clarity, values = ~freq, text=~clarity, domain = list(x = c(0.25, 0.75), y = c(0, 0.6)));

fig %>% layout(title = "Diamonds by cut, clarity and color", showlegend = F)%>%
  add_annotations( x=c(0.2, 0.5, 0.8), y=-0.05, text = c("Cut", "Clarity", "Color"), 
    font = list(size = 15),
    xref = "paper", yref = "paper", xanchor = "center", showarrow = FALSE); # https://plotly.com/r/reference/layout/annotations/

diamonds%>%group_by(cut)%>%summarize(freq = n())->data1;
diamonds%>%group_by(color)%>%summarize(freq = n())->data2;

fig <- plot_ly();
fig<-fig %>%add_pie(data=data1, labels = ~cut, values = ~freq, text=~cut, domain = list(row = 0, column = 0));

fig<-fig %>%add_pie(data=data2, labels = ~color, values = ~freq,text=~color, domain = list(row = 0, column = 1));

fig %>% layout(title = "Diamonds by cut and color", showlegend = F, grid=list(rows=1, columns=2)
)%>%
  add_annotations( x=c(0.2, 0.8), y=0, text = c("Cut", "Color"), 
    font = list(size = 15),
    xref = "paper", yref = "paper", xanchor = "center", showarrow = FALSE);

library(dplyr)
p1 <- plot_ly(diamonds, x = ~price) %>% add_histogram();

p2 <- diamonds %>% group_by(cut)%>%summarise(freq=n())%>%
  plot_ly(x = ~cut, y = ~freq)%>%add_bars(width = 0.4);

fig <- subplot(p1, p2);
fig%>%hide_legend();  #removing the legend due to the merging

p1 <- ggplot(diamonds, aes(x = price)) +
  geom_histogram(fill='blue');
p2 <- ggplot(diamonds, aes(x = cut))+
  geom_bar(fill="orange");
ply1=ggplotly(p1)  
ply2=ggplotly(p2)
subplot(ply1, ply2)

plot_ly(diamonds, x = ~price, type = "histogram",histnorm = "probability", marker = list(color = "blue"));

p=ggplot(diamonds, aes(x = price)) +
  geom_histogram(aes(y= ..density..), fill='blue');
ggplotly(p)

one_plot <- function(d){
  plot_ly(d, x = ~price) %>%
    add_histogram()%>%
    add_annotations(text=~unique(clarity), x = 0.5, y = 1, 
      xref = "paper", yref = "paper", xanchor = "middle",
    yanchor = "top",showarrow = FALSE, 
    font = list(size = 15, face="bold") )
  # https://plotly.com/r/reference/layout/annotations/
      }

diamonds%>%split(.$clarity)%>% #`split` divides the data by the factor variable 
lapply(one_plot)%>%  # `lapply` applies the function
  subplot(nrows = 2, shareX = T, titleX = F) %>% # shareX specifies sharing xlab or not
  hide_legend()

p=ggplot(diamonds, aes(x = price,fill=clarity)) +
  geom_histogram()+
  facet_wrap(~clarity, nrow=2, ncol=4);
ggplotly(p);

plot_ly(diamonds, x = ~cut, color = ~clarity) %>%
  add_histogram(); #both variables are categorical

p=ggplot(diamonds, aes(x = cut, fill = clarity)) +
  geom_bar(position="dodge", stat = "count");  #position="stack"
ggplotly(p)

#diamonds%>%mutate(clarity=fct_rev(clarity));
diamonds%>%count(cut, clarity)%>% group_by(cut)%>%mutate(prop=n/sum(n))%>%
plot_ly(x = ~cut, y=~prop, color = ~clarity)%>%
  add_bars()%>%layout(barmode = "stack");

p=diamonds%>%count(cut, clarity)%>% group_by(cut)%>%mutate(prop=n/sum(n))%>%
ggplot(aes(x = cut, y=prop, fill = clarity)) +
  geom_bar(position="stack", stat = "identity"); 
ggplotly(p)

fit <- density(diamonds$price); #density estimation
diamonds%>%plot_ly(x =~price,type = "histogram", name = "Histogram")%>%
  add_trace(x = fit$x, y = fit$y, type= "scatter",mode = "lines", fill = "tozeroy", #Sets the area to fill with a solid color; "tozeroy" fill to  y=0: https://plotly.com/r/reference/scatter/#scatter-stackgroup
        yaxis = "y2",name =  "Density")%>% 
  layout(yaxis2 = list(overlaying = "y", side = "right"));

p=ggplot(data=diamonds, aes(x=price)) +
geom_histogram(aes(y= ..density..), color="black",fill="white")+
#Histogram with density instead of count on y-axis
geom_density(alpha=.2, fill="red");
ggplotly(p)

plot_ly(diamonds, x=~cut, y=~price, color=~cut)%>%
  add_boxplot()

p=ggplot(diamonds, aes(x=cut, y=price, color=cut))+
  geom_boxplot();
ggplotly(p)

plot_ly(diamonds, x = ~price, y = ~interaction(cut, clarity)) %>%
  add_boxplot(color = ~clarity)%>%
layout(yaxis = list(title = "")) #remove the ylab

p=ggplot(diamonds, aes(x=price, y=interaction(cut, clarity), color = clarity) )+
  geom_boxplot();
p;

#ggplotly(p)

diamonds=diamonds%>%mutate(cc = interaction(clarity, cut));
# interaction levels sorted by median price
lvls <- diamonds%>%group_by(cc)%>%summarise(m = median(price))%>%arrange(m) %>% #order the medians
  pull(cc); #extract the column cc

plot_ly(diamonds, x=~price, y=~factor(cc, lvls))%>% # relevel the factor cc
  add_boxplot(color=~clarity)%>%
  layout(yaxis=list(title=""));

mtcars$cyl=factor(mtcars$cyl);
mtcars%>%plot_ly(y=~mpg, type='violin',color=~cyl, #plot by factor cyl
          box = list(visible = T), #box in the boxplot
          meanline = list(visible = T) #average value
  ) %>%
  layout( title = "Violin plots of mpg by cyl",
    xaxis = list( title = "cylinder")
    );

p=ggplot(data=mtcars, aes(x=cyl,y=mpg) ) +
  geom_violin(aes(fill=cyl))+
  geom_boxplot(width = 0.1, alpha=0.8)+
  labs(title = "Violin plots of mpg by cyl");
ggplotly(p);

mtcars %>%arrange(mpg)->mtcars1;
Fn=ecdf(mtcars1$mpg); # ecdf returns a *function*
mtcars1<-mtcars1%>%mutate(percentiles=Fn(mpg));
#Fn(mtcars1$mpg) #returns the percentiles
mtcars1%>%plot_ly(x = ~mpg, y = ~percentiles,  type = 'scatter', mode = 'lines', name="cdf", 
    line = list(width = 4, color="blue",dash = "dash"));

p=ggplot(mtcars, aes(x=mpg)) +
stat_ecdf(geom = "line", color="blue", size=1);
ggplotly(p);

mtcars%>%mutate(cyl=factor(cyl))%>%group_by(cyl)%>%summarise(mean=mean(mpg), sd=sd(mpg)) %>%
  plot_ly(x=~cyl, y=~mean, color = ~cyl, type = "scatter", mode="markers", error_y = ~list(array = sd) );

p<-mtcars%>%mutate(cyl=factor(cyl))%>%
  group_by(cyl)%>%summarise(mean=mean(mpg),sd=sd(mpg))%>%
  ggplot(aes(x=cyl,y=mean, color=cyl)) +
  geom_point()+
  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd));
ggplotly(p);

mtcars2<-mtcars%>%mutate(cyl=factor(cyl), am=factor(am))%>%
    group_by(cyl,am)%>%summarise(m_mpg=mean(mpg),sd_mpg=sd(mpg));
plot_ly(data=mtcars2[which(mtcars2$am == '0'),], x=~cyl, y=~m_mpg, 
        type = 'scatter', mode = 'markers', name = 'am 0',
        error_y = ~list(array = sd_mpg,color = '#000000')) %>%
  add_trace(data=mtcars2[which(mtcars2$am == '1'),], name = 'am 1');

p=mtcars%>%mutate(cyl=factor(cyl), am=factor(am))%>%
    group_by(cyl,am)%>%summarise(mean=mean(mpg),sd=sd(mpg))%>%
  ggplot(aes(x=cyl,y=mean, color=am)) + geom_point()+
geom_line(aes(group=am))+
geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd));
ggplotly(p);

data(diamonds, package = "ggplot2");
set.seed(26);
diamonds2 = sample_n(diamonds,size=500);
diamonds2%>%plot_ly(x = ~log(carat), y = ~log(price),color=~cut, 
                    type = "scatter",mode = "markers");

p <- ggplot(diamonds2, aes(x = log(carat), y = log(price),color=cut))+
  geom_point();
ggplotly(p);

mtcars%>%mutate(cyl=factor(cyl))%>%
  plot_ly(x = ~mpg, y = ~disp, z = ~cyl) %>%
  add_markers(color = ~cyl)

mtcars%>%mutate(cyl=factor(cyl))%>%
  plot_ly(x = ~mpg, y = ~disp, z = ~wt,color = ~cyl) %>%
  add_markers();

mtcars%>%mutate(cyl=factor(cyl))%>%
  plot_ly(x = ~mpg, y = ~disp, z = ~wt, color = ~cyl) %>%
  add_markers()%>%add_lines();

p=mtcars%>%plot_ly(x = ~mpg, y = ~disp, 
            name='disp', type='scatter', mode='markers') %>%
  add_trace(y = ~hp, name = 'hp', type='scatter', mode='markers', visible=FALSE) %>%
  add_trace(y = ~wt, name = 'wt', type='scatter', mode='markers', visible=FALSE) %>%
 layout(
   title = "Drop down menus - subset variables",
   yaxis = list(title = "disp"),
            updatemenus = list(
            list(
              type= 'dropdownlist',
          buttons = list(
          list(method = "update",
               args = list(list(visible = list(TRUE, FALSE, FALSE)),
                           list(yaxis = list(title = "disp"))),
               label = "disp"),
          list(method = "update",
               args = list(list(visible = list(FALSE, TRUE, FALSE)),
                           list(yaxis = list(title = "hp"))),
               label = "hp"),
          list(method = "update",
               args = list(list(visible = list(FALSE, FALSE, TRUE)),
                           list(yaxis = list(title = "wt"))),
               label = "wt")
          )
        )
      )
    )

p

mtcars%>%plot_ly(x = ~mpg, y = ~disp, 
            name='disp', type='scatter', mode='markers') %>%
  add_trace(y = ~hp, name = 'hp', type='scatter', mode='markers', visible=FALSE) %>%
  add_trace(y = ~wt, name = 'wt', type='scatter', mode='markers', visible=FALSE) %>%
 layout(
   title = "subset variables",
   yaxis = list(title = "disp"),
            updatemenus = list(
            list(
              type= 'buttons',
          buttons = list(
          list(method = "update",
               args = list(list(visible = list(TRUE, FALSE, FALSE)),
                           list(yaxis = list(title = "disp"))),
               label = "disp"),
          list(method = "update",
               args = list(list(visible = list(FALSE, TRUE, FALSE)),
                           list(yaxis = list(title = "hp"))),
               label = "hp"),
          list(method = "update",
               args = list(list(visible = list(FALSE, FALSE, TRUE)),
                           list(yaxis = list(title = "wt"))),
               label = "wt")
          )
        )
      )
    )